In [1]:
# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
In [2]:
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
In [3]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
In [4]:
nltk.download('all')

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[nltk_data]    | Downloading package words to
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[nltk_data]    | Downloading package tagsets to
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[nltk_data]    | Downloading package mte_teip5 to
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[nltk_data]    | Downloading package averaged_perceptron_tagger to
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[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all
Out[4]:
True
Out[4]:
True
In [5]:
# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()
Out[5]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type
0 1 Snowpiercer 2013 18+ 6.9 94% NaN Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... Action,Drama,Sci-Fi,Thriller United States English Set seven years after the world has become a f... 60.0 tv series 3.0 1 0 0 0 1
1 2 Philadelphia 1993 13+ 8.8 80% NaN Charlie Day,Glenn Howerton,Rob McElhenney,Kait... Comedy United States English The gang, 5 raging alcoholic, narcissists run ... 22.0 tv series 18.0 1 0 0 0 1
2 3 Roma 2018 18+ 8.7 93% NaN Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... Action,Drama,History,Romance,War United Kingdom,United States English In this British historical drama, the turbulen... 52.0 tv series 2.0 1 0 0 0 1
3 4 Amy 2015 18+ 7.0 87% NaN Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... Drama United States English A family drama focused on three generations of... 60.0 tv series 6.0 1 0 1 1 1
4 5 The Young Offenders 2016 NaN 8.0 100% NaN Alex Murphy,Chris Walley,Hilary Rose,Dominic M... Comedy United Kingdom,Ireland English NaN 30.0 tv series 3.0 1 0 0 0 1
Out[5]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country Language Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type
0 1 Snowpiercer 2013 18+ 6.9 94% NaN Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... Action,Drama,Sci-Fi,Thriller United States English Set seven years after the world has become a f... 60.0 tv series 3.0 1 0 0 0 1
1 2 Philadelphia 1993 13+ 8.8 80% NaN Charlie Day,Glenn Howerton,Rob McElhenney,Kait... Comedy United States English The gang, 5 raging alcoholic, narcissists run ... 22.0 tv series 18.0 1 0 0 0 1
2 3 Roma 2018 18+ 8.7 93% NaN Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... Action,Drama,History,Romance,War United Kingdom,United States English In this British historical drama, the turbulen... 52.0 tv series 2.0 1 0 0 0 1
3 4 Amy 2015 18+ 7.0 87% NaN Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... Drama United States English A family drama focused on three generations of... 60.0 tv series 6.0 1 0 1 1 1
4 5 The Young Offenders 2016 NaN 8.0 100% NaN Alex Murphy,Chris Walley,Hilary Rose,Dominic M... Comedy United Kingdom,Ireland English NaN 30.0 tv series 3.0 1 0 0 0 1
In [6]:
# profile = ProfileReport(df_tvshows)
# profile
In [7]:
def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()
In [8]:
data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation :
In [9]:
# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)
In [10]:
data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 
No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation :
In [11]:
df_tvshows.head()
Out[11]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Snowpiercer 2013 18 6.9 94 NA Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... Action,Drama,Sci-Fi,Thriller United States ... Set seven years after the world has become a f... 60 tv series 3 1 0 0 0 1 Netflix
1 2 Philadelphia 1993 13 8.8 80 NA Charlie Day,Glenn Howerton,Rob McElhenney,Kait... Comedy United States ... The gang, 5 raging alcoholic, narcissists run ... 22 tv series 18 1 0 0 0 1 Netflix
2 3 Roma 2018 18 8.7 93 NA Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... Action,Drama,History,Romance,War United Kingdom,United States ... In this British historical drama, the turbulen... 52 tv series 2 1 0 0 0 1 Netflix
3 4 Amy 2015 18 7 87 NA Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... Drama United States ... A family drama focused on three generations of... 60 tv series 6 1 0 1 1 1 Netflix
4 5 The Young Offenders 2016 NR 8 100 NA Alex Murphy,Chris Walley,Hilary Rose,Dominic M... Comedy United Kingdom,Ireland ... NA 30 tv series 3 1 0 0 0 1 Netflix

5 rows × 21 columns

Out[11]:
ID Title Year Age IMDb Rotten Tomatoes Directors Cast Genres Country ... Plotline Runtime Kind Seasons Netflix Hulu Prime Video Disney+ Type Service Provider
0 1 Snowpiercer 2013 18 6.9 94 NA Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... Action,Drama,Sci-Fi,Thriller United States ... Set seven years after the world has become a f... 60 tv series 3 1 0 0 0 1 Netflix
1 2 Philadelphia 1993 13 8.8 80 NA Charlie Day,Glenn Howerton,Rob McElhenney,Kait... Comedy United States ... The gang, 5 raging alcoholic, narcissists run ... 22 tv series 18 1 0 0 0 1 Netflix
2 3 Roma 2018 18 8.7 93 NA Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... Action,Drama,History,Romance,War United Kingdom,United States ... In this British historical drama, the turbulen... 52 tv series 2 1 0 0 0 1 Netflix
3 4 Amy 2015 18 7 87 NA Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... Drama United States ... A family drama focused on three generations of... 60 tv series 6 1 0 1 1 1 Netflix
4 5 The Young Offenders 2016 NR 8 100 NA Alex Murphy,Chris Walley,Hilary Rose,Dominic M... Comedy United Kingdom,Ireland ... NA 30 tv series 3 1 0 0 0 1 Netflix

5 rows × 21 columns

In [12]:
df_tvshows.describe()
Out[12]:
ID Year Netflix Hulu Prime Video Disney+ Type
count 5432.000000 5432.000000 5432.000000 5432.000000 5432.000000 5432.000000 5432.0
mean 2716.500000 2010.668446 0.341311 0.293999 0.403351 0.033689 1.0
std 1568.227662 11.726176 0.474193 0.455633 0.490615 0.180445 0.0
min 1.000000 1901.000000 0.000000 0.000000 0.000000 0.000000 1.0
25% 1358.750000 2009.000000 0.000000 0.000000 0.000000 0.000000 1.0
50% 2716.500000 2014.000000 0.000000 0.000000 0.000000 0.000000 1.0
75% 4074.250000 2017.000000 1.000000 1.000000 1.000000 0.000000 1.0
max 5432.000000 2020.000000 1.000000 1.000000 1.000000 1.000000 1.0
Out[12]:
ID Year Netflix Hulu Prime Video Disney+ Type
count 5432.000000 5432.000000 5432.000000 5432.000000 5432.000000 5432.000000 5432.0
mean 2716.500000 2010.668446 0.341311 0.293999 0.403351 0.033689 1.0
std 1568.227662 11.726176 0.474193 0.455633 0.490615 0.180445 0.0
min 1.000000 1901.000000 0.000000 0.000000 0.000000 0.000000 1.0
25% 1358.750000 2009.000000 0.000000 0.000000 0.000000 0.000000 1.0
50% 2716.500000 2014.000000 0.000000 0.000000 0.000000 0.000000 1.0
75% 4074.250000 2017.000000 1.000000 1.000000 1.000000 0.000000 1.0
max 5432.000000 2020.000000 1.000000 1.000000 1.000000 1.000000 1.0
In [13]:
df_tvshows.corr()
Out[13]:
ID Year Netflix Hulu Prime Video Disney+ Type
ID 1.000000 -0.031346 -0.646330 0.034293 0.441264 0.195409 NaN
Year -0.031346 1.000000 0.222316 -0.065807 -0.198675 -0.022741 NaN
Netflix -0.646330 0.222316 1.000000 -0.366515 -0.515086 -0.119344 NaN
Hulu 0.034293 -0.065807 -0.366515 1.000000 -0.377374 -0.075701 NaN
Prime Video 0.441264 -0.198675 -0.515086 -0.377374 1.000000 -0.151442 NaN
Disney+ 0.195409 -0.022741 -0.119344 -0.075701 -0.151442 1.000000 NaN
Type NaN NaN NaN NaN NaN NaN NaN
Out[13]:
ID Year Netflix Hulu Prime Video Disney+ Type
ID 1.000000 -0.031346 -0.646330 0.034293 0.441264 0.195409 NaN
Year -0.031346 1.000000 0.222316 -0.065807 -0.198675 -0.022741 NaN
Netflix -0.646330 0.222316 1.000000 -0.366515 -0.515086 -0.119344 NaN
Hulu 0.034293 -0.065807 -0.366515 1.000000 -0.377374 -0.075701 NaN
Prime Video 0.441264 -0.198675 -0.515086 -0.377374 1.000000 -0.151442 NaN
Disney+ 0.195409 -0.022741 -0.119344 -0.075701 -0.151442 1.000000 NaN
Type NaN NaN NaN NaN NaN NaN NaN
In [14]:
# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)
In [15]:
# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows
In [16]:
# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]
In [17]:
df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]
In [18]:
df_tvshows_title = df_tvshows.copy()
In [19]:
df_tvshows_title.drop(df_tvshows_title.loc[df_tvshows_title['Title'] == "NA"].index, inplace = True)
# df_tvshows_title = df_tvshows_title[df_tvshows_title.Title != "NA"]
In [20]:
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_title_tvshows = df_tvshows_title.loc[df_tvshows_title['Netflix'] == 1]
hulu_title_tvshows = df_tvshows_title.loc[df_tvshows_title['Hulu'] == 1]
prime_video_title_tvshows = df_tvshows_title.loc[df_tvshows_title['Prime Video'] == 1]
disney_title_tvshows = df_tvshows_title.loc[df_tvshows_title['Disney+'] == 1]
In [21]:
plt.figure(figsize = (10, 10))
corr = df_tvshows_title.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>
<Figure size 720x720 with 0 Axes>
In [22]:
df_tvshows_title = df_tvshows_title['Title']
tvshows_title_w = ' '.join(df_tvshows_title)
In [23]:
stopwords = set(STOPWORDS)
 
wordcloud_all_title_tvshows = WordCloud(width = 1000, height = 500,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(tvshows_title_w)
  
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_all_title_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()
In [24]:
tvshows_title_w = tvshows_title_w.lower()

stop_words_english_tvshows = set(STOPWORDS)

word_tokens_english_tvshows = word_tokenize(tvshows_title_w)

filtered_tvshow_title = [w for w in word_tokens_english_tvshows if not w in stop_words_english_tvshows]

filtered_tvshow_title = " ".join(filtered_tvshow_title)

filtered_tvshow_title = re.sub("'s", '', filtered_tvshow_title)

filtered_tvshow_title = re.sub(r'[0-9]+', '', filtered_tvshow_title)

final_tvshow_title = re.sub(r'[^\w\s]', '', filtered_tvshow_title)

title_tvshows_corpus_len = len(filtered_tvshow_title.split())
title_tvshows_corpus_len
Out[24]:
13407
Out[24]:
13407
In [25]:
def extract_ngrams(data, num):
    n_grams = ngrams(nltk.word_tokenize(data), num)
    return [ ' '.join(grams) for grams in n_grams]
In [26]:
title_ngram1_tvshows = FreqDist()

title_ngram1 = extract_ngrams(final_tvshow_title[:title_tvshows_corpus_len], 1)

for word in title_ngram1:
    title_ngram1_tvshows[word.lower()] += 1
In [27]:
title_ngram1_tvshows.most_common(10)
Out[27]:
[('american', 12),
 ('day', 10),
 ('life', 10),
 ('love', 9),
 ('dark', 9),
 ('classic', 9),
 ('albums', 9),
 ('marvel', 9),
 ('black', 8),
 ('man', 8)]
Out[27]:
[('american', 12),
 ('day', 10),
 ('life', 10),
 ('love', 9),
 ('dark', 9),
 ('classic', 9),
 ('albums', 9),
 ('marvel', 9),
 ('black', 8),
 ('man', 8)]
In [28]:
title_ngram2_tvshows = FreqDist()

title_ngram2 = extract_ngrams(final_tvshow_title[:title_tvshows_corpus_len], 2)

for word in title_ngram2:
    title_ngram2_tvshows[word.lower()] += 1
In [29]:
title_ngram2_tvshows.most_common(10)
Out[29]:
[('classic albums', 9),
 ('star trek', 5),
 ('lifetime sessions', 3),
 ('american experience', 3),
 ('next guest', 2),
 ('david letterman', 2),
 ('el camino', 2),
 ('breaking bad', 2),
 ('elles étaient', 2),
 ('étaient en', 2)]
Out[29]:
[('classic albums', 9),
 ('star trek', 5),
 ('lifetime sessions', 3),
 ('american experience', 3),
 ('next guest', 2),
 ('david letterman', 2),
 ('el camino', 2),
 ('breaking bad', 2),
 ('elles étaient', 2),
 ('étaient en', 2)]
In [30]:
title_ngram3_tvshows = FreqDist()

title_ngram3 = extract_ngrams(final_tvshow_title[:title_tvshows_corpus_len], 3)

for word in title_ngram3:
    title_ngram3_tvshows[word.lower()] += 1
In [31]:
title_ngram3_tvshows.most_common(10)
Out[31]:
[('elles étaient en', 2),
 ('étaient en guerre', 2),
 ('kids songs super', 2),
 ('songs super simple', 2),
 ('super simple songs', 2),
 ('happy hour part', 2),
 ('mystery science theater', 2),
 ('snowpiercer philadelphia roma', 1),
 ('philadelphia roma amy', 1),
 ('roma amy young', 1)]
Out[31]:
[('elles étaient en', 2),
 ('étaient en guerre', 2),
 ('kids songs super', 2),
 ('songs super simple', 2),
 ('super simple songs', 2),
 ('happy hour part', 2),
 ('mystery science theater', 2),
 ('snowpiercer philadelphia roma', 1),
 ('philadelphia roma amy', 1),
 ('roma amy young', 1)]
In [32]:
title_ngram4_tvshows = FreqDist()

title_ngram4 = extract_ngrams(final_tvshow_title[:title_tvshows_corpus_len], 4)

for word in title_ngram4:
    title_ngram4_tvshows[word.lower()] += 1
In [33]:
title_ngram4_tvshows.most_common(10)
Out[33]:
[('elles étaient en guerre', 2),
 ('kids songs super simple', 2),
 ('songs super simple songs', 2),
 ('snowpiercer philadelphia roma amy', 1),
 ('philadelphia roma amy young', 1),
 ('roma amy young offenders', 1),
 ('amy young offenders suburra', 1),
 ('young offenders suburra wednesday', 1),
 ('offenders suburra wednesday retribution', 1),
 ('suburra wednesday retribution quincy', 1)]
Out[33]:
[('elles étaient en guerre', 2),
 ('kids songs super simple', 2),
 ('songs super simple songs', 2),
 ('snowpiercer philadelphia roma amy', 1),
 ('philadelphia roma amy young', 1),
 ('roma amy young offenders', 1),
 ('amy young offenders suburra', 1),
 ('young offenders suburra wednesday', 1),
 ('offenders suburra wednesday retribution', 1),
 ('suburra wednesday retribution quincy', 1)]
In [34]:
title_ngram5_tvshows = FreqDist()

title_ngram5 = extract_ngrams(final_tvshow_title[:title_tvshows_corpus_len], 5)

for word in title_ngram5:
    title_ngram5_tvshows[word.lower()] += 1
In [35]:
title_ngram5_tvshows.most_common(10)
Out[35]:
[('kids songs super simple songs', 2),
 ('snowpiercer philadelphia roma amy young', 1),
 ('philadelphia roma amy young offenders', 1),
 ('roma amy young offenders suburra', 1),
 ('amy young offenders suburra wednesday', 1),
 ('young offenders suburra wednesday retribution', 1),
 ('offenders suburra wednesday retribution quincy', 1),
 ('suburra wednesday retribution quincy rainbow', 1),
 ('wednesday retribution quincy rainbow c', 1),
 ('retribution quincy rainbow c word', 1)]
Out[35]:
[('kids songs super simple songs', 2),
 ('snowpiercer philadelphia roma amy young', 1),
 ('philadelphia roma amy young offenders', 1),
 ('roma amy young offenders suburra', 1),
 ('amy young offenders suburra wednesday', 1),
 ('young offenders suburra wednesday retribution', 1),
 ('offenders suburra wednesday retribution quincy', 1),
 ('suburra wednesday retribution quincy rainbow', 1),
 ('wednesday retribution quincy rainbow c', 1),
 ('retribution quincy rainbow c word', 1)]
In [36]:
# Netflix Wordcloud
netflix_title_tvshows_t = netflix_title_tvshows['Title']
netflix_tvshows_title_w = ' '.join(netflix_title_tvshows_t)
In [37]:
stopwords = set(STOPWORDS)
 
wordcloud_netflix_title_tvshows = WordCloud(width = 1000, height = 500,
                                           background_color ='white',
                                           stopwords = stopwords,
                                           min_font_size = 10
                                          ).generate(netflix_tvshows_title_w)

print('\nThe Wordcloud Generated from Titles of Netflix is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_netflix_title_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Titles of Netflix is : 


The Wordcloud Generated from Titles of Netflix is :
In [38]:
# Hulu Wordcloud
hulu_title_tvshows_t = hulu_title_tvshows['Title']
hulu_tvshows_title_w = ' '.join(hulu_title_tvshows_t)
In [39]:
stopwords = set(STOPWORDS)
 
wordcloud_hulu_title_tvshows = WordCloud(width = 1000, height = 500,
                                        background_color ='white',
                                        stopwords = stopwords,
                                        min_font_size = 10
                                       ).generate(hulu_tvshows_title_w)
  
print('\nThe Wordcloud Generated from Titles of Hulu is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_hulu_title_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Titles of Hulu is : 


The Wordcloud Generated from Titles of Hulu is :
In [40]:
# Prime Video Wordcloud
prime_video_title_tvshows_t = prime_video_title_tvshows['Title']
prime_video_tvshows_title_w = ' '.join(prime_video_title_tvshows_t)
In [41]:
stopwords = set(STOPWORDS)
 
wordcloud_prime_video_title_tvshows = WordCloud(width = 1000, height = 500,
                                               background_color ='white',
                                               stopwords = stopwords,
                                               min_font_size = 10
                                              ).generate(prime_video_tvshows_title_w)
  
print('\nThe Wordcloud Generated from Titles of Prime Video is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_prime_video_title_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Titles of Prime Video is : 


The Wordcloud Generated from Titles of Prime Video is :
In [42]:
# Disney+ Wordcloud
disney_title_tvshows_t = disney_title_tvshows['Title']
disney_tvshows_title_w = ' '.join(disney_title_tvshows_t)
In [43]:
stopwords = set(STOPWORDS)
 
wordcloud_disney_title_tvshows = WordCloud(width = 1000, height = 500,
                                          background_color ='white',
                                          stopwords = stopwords,
                                          min_font_size = 10
                                         ).generate(disney_tvshows_title_w)
  
print('\nThe Wordcloud Generated from Titles of Disney+ is : \n')
# plot the WordCloud image                       
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_disney_title_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
  
plt.show()

The Wordcloud Generated from Titles of Disney+ is : 


The Wordcloud Generated from Titles of Disney+ is :